AIF-C01 Security, Compliance and Governance for AI Solutions Practice Test 2 — 15 Questions

Question 1

A machine learning team needs to share a trained model with multiple teams across different AWS accounts. The model artifacts are stored in an S3 bucket in the central account. What is the most secure way to grant cross-account read access to the model artifacts?

Accepted Answer

Use AWS KMS to encrypt the artifacts and share the KMS key with the target accounts, then use bucket policies and IAM roles in the target accounts.. Option D is correct because it implements a defense-in-depth approach: AWS KMS encrypts the model artifacts at rest, and cross-account access is granted by combining an S3 bucket policy that allows the target accounts' IAM roles to read the objects, with those roles assuming the necessary permissions. This ensures that only authenticated and authorized IAM principals in the target accounts can decrypt and access the artifacts, preventing unauthorized access even if the bucket policy is misconfigured.

Answer

Use an S3 bucket policy that grants access to the root user of each target account.

Answer

Make the S3 bucket public.

Answer

Use S3 cross-region replication to copy the artifacts to each target account's bucket.

Question 2

A company is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket that is encrypted with an AWS KMS customer managed key (CMK). The training job fails with an error indicating that the SageMaker execution role does not have permission to decrypt the data. The administrator has verified that the execution role has the kms:Decrypt permission. What additional permission might be missing?

Accepted Answer

kms:CreateGrant. When SageMaker accesses encrypted S3 data, it must create a grant on the KMS key to allow the SageMaker service principal to decrypt the data on behalf of the execution role. Even if the execution role has kms:Decrypt, SageMaker itself needs permission to call kms:CreateGrant to set up this delegated access. Without kms:CreateGrant, the service cannot establish the necessary cryptographic context for the training job.

Answer

kms:ReEncrypt

Answer

kms:DescribeKey

Answer

kms:GenerateDataKey

Question 3

A company wants to monitor for malicious activity in their machine learning pipelines, such as unauthorized access to training data or model artifacts. Which AWS service can provide automated threat detection and continuous monitoring?

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior across AWS workloads, including machine learning pipelines. It uses machine learning, anomaly detection, and integrated threat intelligence to identify threats such as unauthorized access to S3 buckets containing training data or model artifacts, without requiring manual intervention.

Answer

AWS Config

Answer

AWS Shield

Answer

Amazon Inspector

Question 4

A data scientist is using Amazon SageMaker Studio. The company policy requires that all data used in SageMaker Studio notebooks be encrypted at rest and in transit. Which configuration should be enabled to meet this requirement?

Accepted Answer

Enable SageMaker Studio's data encryption using a customer managed key (CMK) and configure the notebook to use HTTPS for all traffic.. Option D is correct because it addresses both encryption at rest and in transit. SageMaker Studio uses Amazon EFS for persistent storage, and enabling encryption at rest with a customer managed key (CMK) ensures data on the EFS file system is encrypted. Additionally, configuring the notebook to use HTTPS ensures all traffic between the notebook and other services is encrypted in transit, meeting the company policy.

Answer

Store all data in encrypted DynamoDB tables and query from notebooks.

Answer

Use a VPC with no internet access and enable S3 encryption for all data.

Answer

Enable SageMaker Studio's default encryption using the AWS managed key for EFS.

Question 5

An organization uses Amazon Bedrock to generate content. They have implemented guardrails to block toxic content. However, some users are able to bypass the guardrails by encoding their prompts. What step should be taken to improve security?

Accepted Answer

Enable prompt injection detection in the guardrail configuration.. Option B is correct because Amazon Bedrock guardrails include a built-in prompt injection detection capability that can identify and block attempts to bypass content filters through encoded or obfuscated prompts. Enabling this feature specifically addresses the scenario where users encode their inputs to evade toxic content blocking, as it analyzes the decoded intent of the prompt rather than just the surface-level encoding.

Answer

Encode the prompts before sending to the model.

Answer

Use a different foundation model that is less susceptible.

Answer

Restrict access to the model using IAM policies.

Question 6

A company is deploying a machine learning model that processes personal data of EU citizens. Which AWS service can help them comply with the General Data Protection Regulation (GDPR) by tracking data lineage and usage?

Accepted Answer

AWS Audit Manager. AWS Audit Manager is the correct service because it provides prebuilt frameworks and automated evidence collection to help you audit and demonstrate compliance with regulations like GDPR. It specifically tracks data lineage and usage by continuously collecting and organizing evidence from your AWS resources, which is essential for GDPR's requirements on data processing transparency and accountability.

Answer

AWS CloudTrail

Answer

AWS Config

Answer

Amazon SageMaker Model Registry

Question 7

A machine learning engineer wants to ensure that a SageMaker notebook instance only has access to a specific S3 bucket containing training data. The notebook instance is in a VPC. What is the most secure way to restrict access?

Accepted Answer

Use a VPC endpoint for S3 and a bucket policy that restricts access to the VPC endpoint.. Option A is correct because using a VPC endpoint for S3 combined with a bucket policy that restricts access to that specific endpoint ensures that only traffic originating from within the VPC (and thus from the SageMaker notebook instance) can reach the S3 bucket. This approach enforces network-level isolation and prevents access from any other source, including the public internet, even if the IAM role is compromised. It is the most secure method because it layers network policy (VPC endpoint) with resource-based policy (bucket policy) to create a tightly scoped access control.

Answer

Place the notebook instance in a private subnet with a NAT gateway.

Answer

Use AWS KMS to encrypt the bucket and grant the notebook role decrypt permissions.

Answer

Assign an IAM role to the notebook with an S3 bucket policy that only allows access to that bucket.

Question 8

A company uses Amazon SageMaker for model training. To comply with data residency requirements, they must ensure that the training data never leaves a specific AWS region. However, during training, the SageMaker service might use resources in other regions for auto-scaling. Which configuration should they use to enforce data residency?

Accepted Answer

Use AWS Organizations to create an SCP that denies access to SageMaker resources in other regions.. Option C is correct because AWS Organizations Service Control Policies (SCPs) can explicitly deny access to SageMaker resources in any region outside the allowed one. By attaching an SCP that denies `sagemaker:*` actions when the `aws:RequestedRegion` condition key does not match the permitted region, the company can enforce data residency at the account level, preventing SageMaker from provisioning resources in other regions even if auto-scaling would otherwise trigger cross-region activity.

Answer

Configure the training job to use only local spot instances and enable network isolation.

Answer

Use Amazon SageMaker's inter-container traffic encryption and disable cross-region data transfer.

Answer

Use a VPC with a VPC endpoint for SageMaker and restrict the training job to use only local resources.

Question 9

A company is using Amazon Bedrock to deploy a chatbot. They want to ensure that the chatbot does not produce harmful or biased content. Which TWO AWS services or features can be used together to implement content moderation and monitoring?

Accepted Answer

Amazon Bedrock Guardrails for content filtering. Amazon Bedrock Guardrails (Option D) is correct because it provides built-in content filtering capabilities specifically designed for foundation models, allowing you to define denied topics, filter harmful content, and enforce safety policies directly within the Bedrock chatbot workflow. Amazon CloudWatch Logs (Option E) is correct because it enables you to log model inputs and outputs, which can be monitored for compliance, audited for bias, and used to trigger alerts when harmful content is detected. Together, they form a comprehensive content moderation and monitoring solution that addresses both proactive filtering and reactive analysis.

Answer

Amazon SageMaker Model Monitor for drift detection

Answer

Amazon Rekognition for image moderation

Answer

Amazon Comprehend for sentiment analysis

Question 10

A financial services company is deploying a machine learning model that must comply with SOC 2 and PCI DSS. They need to ensure that the model artifacts and training data are encrypted, access is audited, and the environment is protected from network threats. Which THREE AWS services should they implement?

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior, which helps protect the environment from network threats as required by SOC 2 and PCI DSS. By analyzing VPC Flow Logs, DNS logs, and CloudTrail events, it can detect anomalies such as port scanning or data exfiltration, directly addressing the need for network threat protection in a compliant ML deployment.

Answer

Amazon DynamoDB Accelerator (DAX)

Answer

AWS WAF

Question 11

A data science team is using Amazon SageMaker to build a model. They want to ensure that only authorized users can deploy models to production. Which TWO methods can they use to enforce this?

Accepted Answer

Use SageMaker Model Registry to require approval before deployment.. Option A is correct because SageMaker Model Registry allows you to set up an approval workflow for model versions. By requiring explicit approval before a model can be deployed to production, you enforce a governance gate that prevents unauthorized or unverified models from being used in production endpoints.

Answer

Enable multi-factor authentication (MFA) for all AWS accounts.

Answer

Use AWS CloudTrail to audit deployment actions.

Answer

Use Amazon GuardDuty to monitor for unauthorized deployment.

Question 12

Refer to the exhibit. A security officer has attached this IAM policy to a user to allow invocation of a SageMaker endpoint. However, the user is unable to invoke the endpoint from an EC2 instance in the same VPC. What is the most likely cause?

Accepted Answer

The EC2 instance is not using the specified VPC endpoint (vpce-12345678).. The policy condition requires that the request originates from a specific VPC endpoint (vpce-12345678). If the EC2 instance is not using that endpoint (e.g., it is using a different endpoint or direct VPC access), the request will be denied. Option B is correct because the instance likely does not use that specific VPC endpoint.

Answer

The SageMaker endpoint is not configured to accept traffic from the VPC.

Answer

The IAM policy does not allow the sagemaker:InvokeEndpoint action without conditions.

Answer

The user's IAM role is missing the s3:GetObject permission for model artifacts.

Question 13

Refer to the exhibit. A data scientist is trying to run a SageMaker training job that uses training data from an S3 bucket encrypted with the specified KMS key. The training job fails with an access denied error. The IAM role shown is attached to the training job. What is the missing permission?

Accepted Answer

kms:CreateGrant. When a SageMaker training job accesses encrypted S3 data, it must be granted permission to use the KMS key for decryption. The `kms:CreateGrant` permission allows SageMaker to create a grant on the KMS key, which is required for the service to decrypt the data on behalf of the user. Without this permission, the training job fails with an access denied error even if other KMS actions are allowed.

Answer

s3:ListBucket

Answer

kms:GenerateDataKey

Answer

s3:GetBucketLocation

Question 14

Refer to the exhibit. A security analyst is reviewing CloudTrail logs and notices a training job creation from an IP address (203.0.113.5) that is not associated with the company's network. What is the most likely cause?

Accepted Answer

The user john.doe is accessing the AWS Management Console from a VPN.. The IP address 203.0.113.5 is a non-routable test IP (RFC 5737) and not associated with the company's network. The most likely cause is that user john.doe is accessing the AWS Management Console through a VPN, which would route traffic through the VPN's public IP rather than the corporate network. This explains why the source IP appears external while the user identity is legitimate.

Answer

The CloudTrail log is being generated by a cross-account role.

Answer

The training job was created using the AWS CLI from an external machine.

Answer

The training job was created by a malicious actor who stole credentials.

Question 15

A company is using Amazon Bedrock to deploy a foundation model. To comply with GDPR, they need to ensure that the model does not generate outputs containing personally identifiable information (PII). Which AWS service can best help detect and redact PII from the model's responses?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is the correct service because it provides a built-in PII detection and redaction capability that can be integrated with Amazon Bedrock. Using the `DetectPiiEntities` API, you can scan model responses for PII such as names, addresses, and credit card numbers, and then redact or mask those entities before returning the output to the user. This directly addresses the GDPR requirement to prevent PII leakage from generative AI outputs.

Answer

Amazon GuardDuty

Answer

Amazon Rekognition

Answer

AWS WAF